Predicting Source Gaze Fixation duration

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Predicting Source Gaze Fixation duration

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Title: Predicting Source Gaze Fixation duration
A Machine Learning Approach
Author: Saikh, Tanik; Bangalore, Srinivas; Carl, Michael; Bandyopadhyay, Sivaji
Abstract: In this paper an attempt has been made to predict the gaze fixation duration at source text using supervised learning techniques. The machine learning models used in the present work make use of lexical, syntactic and semantic information for predicting the gaze fixation duration. Different features are extracted from the data and models are built by combining the features. Our best set up achieves close to 50% classification accuracy.
URI: http://hdl.handle.net/10398/9121
Date: 2015-04-23
Notes: Paper presented at the 2015 International Conference on Cognitive Computing and Information Processing. 3-4 March 2015. Noida, Indien

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